AB1530 USING MACHINE LEARNING TO ANALYZE THE FUNDOSCOPY OF ANKYLOSING SPONDYLITIS, PSORIATIC ARTHRITIS AND PSORIASIS
Authors: Y. J. Huang, Y. H. Huang, Y. C. Kang, Z. Y. Liu, J. S. Chen, C. H. Lin, C. F. Kuo
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Autoimmune diseases are associated with an increased risk of cardiovascular disease which can be preceded by microvasculature changes. The study about fundoscopy in ankylosing spondylitis is described by Bentum et al.[1] but the related study about deep learning model is insufficient. <h3>Objectives</h3> To validate the ability of machine learning model to verify the fundoscopy of ankylosing spondylitis, psoriatic arthritis, psoriasis and normal control. <h3>Methods</h3> We collected 1864 color fundus exams of 1105 patients with ankylosing spondylitis, psoriatic arthritis and psoriasis in Chang Gung Memorial Hospital in Taiwan from January 2001 to June 2022. The control group is 7122 fundoscopies of 5525 patients matched with age and gender. We separated database into training group, validation group and testing group. (7:1.5:1.5). We trained two convolutional neural networks including Vgg-11 and ResNet-18 models to perform the segmentation and capture the obscure findings of fundoscopy that human eyes cannot identify. We use the sensitivity, specificity, AUC, odds ratio and positive likelyhood ratio to evaluate the performance of machine learning to diagnose ankylosing spondylitis, psoriatic arthritis and psoriasis. <h3>Results</h3> The ankylosing spondylitis, psoriatic arthritis and psoriasis patients have higher body mass index, disease-modifying antirheumatic drugs and biological agent's usage, and even more comorbidities with dyslipidemia, hypertension and coronary artery disease compared with control group. In the setting of one case with solitary image of fundoscopy, the machine learning model of ResNet-18 showed AUC 0.78 with sensitivity 82% and specificity 66 %. In the setting of one case with solitary image of fundoscopy, the machine learning model of Vgg-11 showed the AUC 0.76 with sensitivity 79% and specificity 64%. The odds ratio is 8.54 and the positive likelihood ratio is 2.41 for deep learning model to predict the diagnosis of diseases. <h3>Conclusion</h3> The performance of machine learning model to predict the diagnosis of ankylosing spondylitis, psoriatic arthritis and psoriasis is quite acceptable. Fundoscopy analyzed with deep learning model may be a useful tool to help us to stratify the patient of ankylosing spondylitis, psoriatic arthritis and psoriasis. <h3>Reference</h3> [1]van Bentum, R.E., et al., <i>Microvascular changes of the retina in ankylosing spondylitis, and the association with cardiovascular disease - the eye for a heart study.</i> Semin Arthritis Rheum, 2020. <b>50</b>(6): p. 1535-1541. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Yun-Ju Huang Grant/research support from: This study is partially sponsored by the AbbVie Incorporation., Yu-Huei Huang Grant/research support from: This study is partially sponsored by the AbbVie Incorporation., Yu-Chuan Kang Grant/research support from: This study is partially sponsored by the AbbVie Incorporation., Zhi-Yong Liu Grant/research support from: This study is partially sponsored by the AbbVie Incorporation., Jung-Sheng Chen Grant/research support from: This study is partially sponsored by the AbbVie Incorporation., Chi-hung Lin Grant/research support from: This study is partially sponsored by the AbbVie Incorporation., Chang-Fu Kuo Grant/research support from: This study is partially sponsored by the AbbVie Incorporation.